What we do:
Flagright helps fintech startups prevent financial crime and remain AML compliant while enabling them to grow revenue and manage risk appetite in real-time. It’s an API-first solution with a no-code back office portal. We provide real-time responses even for data-intensive, complex rules via a simple and extensible API on a usage-based pricing model. We are on a mission to commoditize financial crime prevention tooling for startups globally and we're looking for an experienced Frontend Software Engineer to join our team.
We are a Y Combinator-backed company based in Berlin, serving customers from 6 continents.
About the role:
As a Data Science Intern at Flagright, you will be part of building the first iteration of Flagright’s AI/ML Systems. You will work on setting up the core foundation of data science operations and processes within Flagright and come up with efficient and automated data quality controls and track the causes of any possible issues through data flows.
This is a remote position, so you will need to be self-motivated and able to work independently under minimal supervision. At Flagright, we offer exciting career growth opportunities for motivated individuals who are looking to take the next step in their careers. We have a very open and transparent culture, so you will also have the opportunity to contribute to any aspect of the business. Nothing here is beyond your reach.
If you are highly driven, enthusiastic, and looking for an exciting opportunity to join an early-stage startup, we want to hear from you.
🛠 You will
- Research, develop, design, and build models for threat detection, guiding processes for signal ingestion, data analytics, and automation to improve detection and investigation of potentially malicious behaviors pertaining to financial transactions.
- Build statistical, machine learning, and simulation models on large datasets, including unstructured data from disparate sources;
- Solve real world problems pertaining to anti-fraud systems and financial crime prevention using machine learning
- Drive the creation, collection and processing of new data and the enrichment of existing data sources
- Develop technical and functional requirements to deploy novel detection and vulnerability identification capabilities that mitigate emergent and current threats.
- Work closely with customers to deploy ML models, collect feedback and build a data collection and enrichment process.
🙌 Your profile
- PHD candidate in data science, statistics, economics, engineering, finance, mathematics or a relative quantitative field at a university in the US, UK, or Europe (Required)
- A proven track record of translating large and ambiguous business problems in mathematical models and developing data scientific solutions.
- Good understanding of databases, data structures and testing methodologies
- Experience in Python programming, including libraries like Pandas, Numpy, sklearn, OR-tools
- Excellent communication skills in English, both written and verbal
- Analytical mindset, strong problem-solving and communication skills paired with reliability
💯 Preferred Qualifications
- Experience working on data cleaning and data preparation
- Strong preference for backgrounds in the financial sector
- Strong knowledge of undergraduate statistics: distributions, uncertainty, biases, hypothesis testing, etc
- 2+ years of experience as a Data Scientist or similar position.
- Knowledge and/or experience (professional or non-professional) in LLMs
🤗 Benefits
- Work alongside a highly competent, top-tier team (Y Combinator, ex Palantir, AWS, Revolut, Zalando).
- Low bureaucracy, minimal meetings, async communications culture, international culture, flat organization.
*Do something meaningful; help stop human trafficking, money laundering, and child labor; Be a part of enabling the future of how money moves.
- Great career development opportunities in a fast-growing company with challenging and unique business problems.
If you would like to be a part of enabling the future of fintech startups, please apply!
Flagright
